Thesaurus-Based Method of Increasing Text-via-Keyphrase Graph Connectivity During Keyphrase Extraction for e-Tourism Applications

Paramonov, Ilya; Lagutina, Ksenia; Mamedov, Eldar; Lagutina, Nadezhda

doi:10.1007/978-3-319-45880-9_11

Cited by 10 publications

(5 citation statements)

References 7 publications

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“…Shouzhong et al [53] integrates TF-IDF and TextRank to mine and analyze personal interests from Weibo text. Paramonov et al [54] developed a new method combining well-known keyword extraction algorithms (e.g., TextRank and Topic PageRank) and a thesaurus-based procedure, thereby improving the connectivity of the text-via-keyphrase graph while also increasing the accuracy and recall rate of key phrase extraction. Gagliardi et al [55] integrated the word embedding model and clustering algorithm to establish a novel method capable of automatically extracting keywords/phrases from text without supervision.…”

Section: Keyword Extraction and Semantic Expansionmentioning

confidence: 99%

Research on Sentiment Classification of Online Travel Review Text

Wen

Zheng

et al. 2020

Applied Sciences

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In recent years, the number of review texts on online travel review sites has increased dramatically, which has provided a novel source of data for travel research. Sentiment analysis is a process that can extract tourists’ sentiments regarding travel destinations from online travel review texts. The results of sentiment analysis form an important basis for tourism decision making. Thus far, there has been minimal concern as to how sentiment analysis methods can be effectively applied to improve the effect of sentiment analysis. However, online travel review texts are largely short texts characterized by uneven sentiment distribution, which makes it difficult to obtain accurate sentiment analysis results. Accordingly, in order to improve the sentiment classification accuracy of online travel review texts, this study transformed sentiment analysis into a multi-classification problem based on machine learning methods, and further designed a keyword semantic expansion method based on a knowledge graph. Our proposed method extracts keywords from online travel review texts and obtains the concept list of keywords through Microsoft Knowledge Graph. This list is then added to the review text to facilitate the construction of semantically expanded classification data. Our proposed method increases the number of classification features used for short text by employing the huge corpus of information associated with the knowledge graph. In addition, this article introduces online travel review text preprocessing, keyword extraction, text representation, sampling, establishment classification labeling, and the selection and application of machine learning-based sentiment classification methods in order to build an effective sentiment classification model for online travel review text. Experiments were implemented and evaluated based on the English review texts of four famous attractions in four countries on the TripAdvisor website. Our experimental results demonstrate that the method proposed in this paper can be used to effectively improve the accuracy of the sentiment classification of online travel review texts. Our research attempts to emphasize and improve the methodological relevance and applicability of sentiment analysis for future travel research.

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Section: Keyword Extraction and Semantic Expansionmentioning

confidence: 99%

Research on Sentiment Classification of Online Travel Review Text

Wen

Zheng

et al. 2020

Applied Sciences

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“…ключевые слова. Алгоритмы выделения ключевых слов были исследованы в предыдущей работе авторов [4]. По результатам исследования обучаемые алгоритмы работают более эффективно, чем алгоритмы без учителя, но составление материала для обучения требует огромных затрат со стороны эксперта-лингвиста.…”

Section: выделение терминовunclassified

RussianLanguage Thesauri: Automated Construction and Application For Natural Language Processing Tasks

Lagutina¹,

Lagutina²,

Адрианов³

et al. 2018

Model. anal. inf. sist.

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The paper reviews the existing Russian-language thesauri in digital form and methods of their automatic construction and application. The authors analyzed the main characteristics of open access thesauri for scientific research, evaluated trends of their development, and their effectiveness in solving natural language processing tasks. The statistical and linguistic methods of thesaurus construction that allow to automate the development and reduce labor costs of expert linguists were studied. In particular, the authors considered algorithms for extracting keywords and semantic thesaurus relationships of all types, as well as the quality of thesauri generated with the use of these tools. To illustrate features of various methods for constructing thesaurus relationships, the authors developed a combined method that generates a specialized thesaurus fully automatically taking into account a text corpus in a particular domain and several existing linguistic resources. With the proposed method, experiments were conducted with two Russian-language text corpora from two subject areas: articles about migrants and tweets. The resulting thesauri were assessed by using an integrated assessment developed in the previous authors’ study that allows to analyze various aspects of the thesaurus and the quality of the generation methods. The analysis revealed the main advantages and disadvantages of various approaches to the construction of thesauri and the extraction of semantic relationships of different types, as well as made it possible to determine directions for future study.

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“…Оценка связности тезауруса выполняется по примеру предыдущей работы авто-ров [6] и обосновывается тем фактом, что для успешного применения тезауруса в большинстве случаев решающее значение имеет навигация по связям. В частности, авторы предлагают использовать характеристики графа, определенного выше.…”

Section: общая схема построения тезаурусаunclassified

“…В работе [6] авторы данной статьи исследуют выбранные стандартные методы для выделения ключевых слов на корпусе текстов о туристических объектах Ка-релии. Данный корпус из 900 текстов удобен для анализа методов решения задачи построения узкоспециализированного тезауруса в частности тем, что содержит вы-борку из 200 текстов с выделенными вручную ключевыми словами.…”

Section: методы выделения терминов тезаурусаunclassified

“…Можно сделать вывод, что для повышения качества выделения ключевых слов в конкретной предметной области требуется существен-ная доработка стандартных алгоритмов. Авторами был предложен алгоритм выде-ления ключевых слов, учитывающий особенности предметной области туризма, а также особенности информационной системы, для которой он разрабатывался [6]. Аналогичный подход может быть использован для повышения качества выделения терминов специализированного тезауруса.…”

Section: методы выделения терминов тезаурусаunclassified

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